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解码神经反馈调节的认知唤醒状态,以研究耶克斯-多德森定律对表现的调节作用。

Decoding a Neurofeedback-Modulated Cognitive Arousal State to Investigate Performance Regulation by the Yerkes-Dodson Law.

出版信息

Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:6551-6557. doi: 10.1109/EMBC46164.2021.9629764.

DOI:10.1109/EMBC46164.2021.9629764
PMID:34892610
Abstract

Enhancing the productivity of humans by regulating arousal during cognitive tasks is a challenging topic in psychology that has a great potential to transform workplaces for increased productivity and educational systems for enhanced performance. In this study, we assess the feasibility of using the Yerkes-Dodson law from psychology to improve performance during a working memory experiment. We employ a Bayesian filtering approach to track cognitive arousal and performance. In particular, by utilizing skin conductance signal recorded during a working memory experiment in the presence of music, we decode a cognitive arousal state. This is done by considering the rate of neural impulse occurrences and their amplitudes as observations for the arousal model. Similarly, we decode a performance state using the number of correct and incorrect responses, and the reaction time as binary and continuous behavioral observations, respectively. We estimate the arousal and performance states within an expectation-maximization framework. Thereafter, we design an arousal-performance model on the basis of the Yerkes-Dodson law and estimate the model parameters via regression analysis. In this experiment musical neurofeedback was used to modulate cognitive arousal. Our investigations indicate that music can be used as a mode of actuation to influence arousal and enhance the cognitive performance during working memory tasks. Our findings can have a significant impact on designing future smart workplaces and online educational systems.

摘要

通过调节认知任务中的觉醒来提高人类的生产力是心理学中的一个具有挑战性的课题,它具有极大的潜力,可以提高工作场所的生产力和教育系统的绩效。在这项研究中,我们评估了从心理学中借用耶基斯-多德森定律来提高工作记忆实验期间表现的可行性。我们采用贝叶斯滤波方法来跟踪认知唤醒和表现。具体来说,我们利用在音乐存在下进行工作记忆实验期间记录的皮肤电导率信号,对认知唤醒状态进行解码。这是通过将神经冲动的发生率及其幅度作为唤醒模型的观察值来完成的。同样,我们使用正确和错误反应的数量以及反应时间作为二进制和连续行为观察值来解码表现状态。我们在期望最大化框架内估计唤醒和表现状态。然后,我们根据耶基斯-多德森定律设计唤醒-表现模型,并通过回归分析估计模型参数。在这个实验中,音乐神经反馈被用来调节认知唤醒。我们的研究表明,音乐可以作为一种激励方式来影响唤醒,并在工作记忆任务中提高认知表现。我们的发现可以对设计未来的智能工作场所和在线教育系统产生重大影响。

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